Papers Multi-Label Classification

BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection

Conditional Graphical Lasso for Multi-Label Image Classification

Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification perfor…

Weakly Supervised Object Localization With Progressive Domain Adaptation

We address the problem of weakly supervised object localization where only image-level annotations are available for training. Many existing approaches tackle this problem through object proposal mining. However, a subst…

Multi-Label Ranking From Positive and Unlabeled Data

In this paper, we specifically examine the training of a multi-label classifier from data with incompletely assigned labels. This problem is fundamentally important in many multi-label applications because it is almost i…

Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion

Advanced computer vision and machine learning techniques tried to automatically categorize the emotions elicited by abstract paintings with limited success. Since the annotation of the emotional content is highly resourc…

Classification of Human Whole-Body Motion using Hidden Markov Models

Human motion plays an important role in many fields. Large databases exist that store and make available recordings of human motions. However, annotating each motion with multiple labels is a cumbersome and error-prone p…

F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets

We discuss a method to improve the exact F-measure maximization algorithm called GFM, proposed in (Dembczynski et al. 2011) for multi-label classification, assuming the label set can be can partitioned into conditionally…

CNN-RNN: A Unified Framework for Multi-label Image Classification

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to…

Towards Label Imbalance in Multi-label Classification with Many Labels

In multi-label classification, an instance may be associated with a set of labels simultaneously. Recently, the research on multi-label classification has largely shifted its focus to the other end of the spectrum where …

Topic Model Based Multi-Label Classification from the Crowd

Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes. The key challenge in this problem is learning the correlations between the classes. An a…

Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories

In this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to de…

Cost-Sensitive Label Embedding for Multi-Label Classification

Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different c…

Fast Training of Triplet-based Deep Binary Embedding Networks

In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss becau…

Asymptotic consistency and order specification for logistic classifier chains in multi-label learning

Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in which the conditional probabilities are…

Feature ranking for multi-label classification using Markov Networks

We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final …

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling i…

LLSF - Learning Label Specific Features for Multi-Label Classifcation

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework, …

Inducing Generalized Multi-Label Rules with Learning Classifier Systems

In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications, such as text classification and medical diagnoses. Although sparsely studied in this context,…

On the Optimality of Classifier Chain for Multi-label Classification

To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain…

Sparse Local Embeddings for Extreme Multi-label Classification

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…